用视觉识别流体表面变形，非接触测粘度，适合自动化实验室。
ViscNet: Vision-Based In-line Viscometry for Fluid Mixing Process
- 通过背景图案在流体表面折射变形推断粘度，无需接触。
- 粘度回归平均误差0.113 log m²/s，分类准确率达81%。
- 支持不确定性评估，多图案设计提升抗干扰能力。
粘度测量对过程监控和自动化实验至关重要，但传统粘度计具有侵入性，需受控实验环境，与真实工况差异大。本文提出一种基于计算机视觉的非接触式粘度计，通过分析固定背景图案随混合驱动下连续变形自由表面产生的光学畸变来推断粘度。在多种光照条件下，该系统在粘度回归任务中均方绝对误差为0.113（单位：log m²/s），粘度类别预测最高准确率达81%。尽管粘度值接近的类别性能下降，但采用多图案策略可提供更丰富的视觉线索以增强鲁棒性。为确保传感器可靠性，系统引入不确定性量化，实现带置信度估计的粘度预测。该非接触式粘度计为现有方法提供了实用且适配自动化的替代方案。
原文摘要 · Abstract (English)
Viscosity measurement is essential for process monitoring and autonomous laboratory operation, yet conventional viscometers remain invasive and require controlled laboratory environments that differ substantially from real process conditions. We present a computer-vision-based viscometer that infers viscosity by exploiting how a fixed background pattern becomes optically distorted as light refracts through the mixing-driven, continuously deforming free surface. Under diverse lighting conditions, the system achieves a mean absolute error of 0.113 in log m2 s^-1 units for regression and reaches up to 81% accuracy in viscosity-class prediction. Although performance declines for classes with closely clustered viscosity values, a multi-pattern strategy improves robustness by providing enriched visual cues. To ensure sensor reliability, we incorporate uncertainty quantification, enabling viscosity predictions with confidence estimates. This stand-off viscometer offers a practical, automation-ready alternative to existing viscometry methods.
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